Overview

Dataset statistics

Number of variables23
Number of observations2938
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory528.0 KiB
Average record size in memory184.0 B

Variable types

Numeric21
Categorical2

Variable descriptions

filesFiles in the filesystem
datecCreation date
datemModification date

Alerts

Country has a high cardinality: 193 distinct valuesHigh cardinality
Adult_Mortality is highly overall correlated with HIV/AIDS and 2 other fieldsHigh correlation
Alcohol is highly overall correlated with Education and 1 other fieldsHigh correlation
BMI is highly overall correlated with Education and 6 other fieldsHigh correlation
Diphtheria is highly overall correlated with Education and 4 other fieldsHigh correlation
Education is highly overall correlated with Alcohol and 12 other fieldsHigh correlation
GDP is highly overall correlated with Education and 3 other fieldsHigh correlation
HepB is highly overall correlated with Diphtheria and 1 other fieldsHigh correlation
HIV/AIDS is highly overall correlated with Adult_Mortality and 5 other fieldsHigh correlation
Income_Composition is highly overall correlated with Adult_Mortality and 12 other fieldsHigh correlation
Infant_Deaths is highly overall correlated with Education and 4 other fieldsHigh correlation
Life_Expectancy is highly overall correlated with Adult_Mortality and 12 other fieldsHigh correlation
lt5_Deaths is highly overall correlated with BMI and 6 other fieldsHigh correlation
Measles is highly overall correlated with Infant_Deaths and 1 other fieldsHigh correlation
Percent_Expenditure is highly overall correlated with GDPHigh correlation
Polio is highly overall correlated with Diphtheria and 4 other fieldsHigh correlation
Thinness_1-19y is highly overall correlated with BMI and 4 other fieldsHigh correlation
Thinness_5-9y is highly overall correlated with BMI and 4 other fieldsHigh correlation
Dev_Status is highly overall correlated with Alcohol and 3 other fieldsHigh correlation
Unnamed: 0 is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique
Income_Composition has 133 (4.5%) zerosZeros
Infant_Deaths has 848 (28.9%) zerosZeros
lt5_Deaths has 785 (26.7%) zerosZeros
Measles has 983 (33.5%) zerosZeros
Percent_Expenditure has 611 (20.8%) zerosZeros

Reproduction

Analysis started2023-01-06 05:11:09.685218
Analysis finished2023-01-06 05:11:59.663583
Duration49.98 seconds
Software versionpandas-profiling vdev
Download configurationconfig.json

Variables

Adult_Mortality
Real number (ℝ)

Distinct425
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164.81654
Minimum1
Maximum723
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-06T00:11:59.748191image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q174
median144
Q3228
95-th percentile411
Maximum723
Range722
Interquartile range (IQR)154

Descriptive statistics

Standard deviation124.43375
Coefficient of variation (CV)0.75498337
Kurtosis1.7264088
Mean164.81654
Median Absolute Deviation (MAD)76
Skewness1.1701818
Sum484231
Variance15483.758
MonotonicityNot monotonic
2023-01-06T00:11:59.907399image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 34
 
1.2%
14 30
 
1.0%
16 29
 
1.0%
138 25
 
0.9%
11 25
 
0.9%
19 24
 
0.8%
144 22
 
0.7%
13 21
 
0.7%
17 21
 
0.7%
15 21
 
0.7%
Other values (415) 2686
91.4%
ValueCountFrequency (%)
1 12
0.4%
2 8
 
0.3%
3 6
 
0.2%
4 4
 
0.1%
5 2
 
0.1%
6 13
0.4%
7 16
0.5%
8 14
0.5%
9 12
0.4%
11 25
0.9%
ValueCountFrequency (%)
723 1
< 0.1%
717 1
< 0.1%
715 1
< 0.1%
699 1
< 0.1%
693 1
< 0.1%
686 1
< 0.1%
682 1
< 0.1%
679 1
< 0.1%
675 1
< 0.1%
666 1
< 0.1%

Alcohol
Real number (ℝ)

Distinct1076
Distinct (%)36.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6072703
Minimum0.01
Maximum17.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-06T00:12:00.045635image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.01
Q10.88
median3.765
Q37.665
95-th percentile11.963
Maximum17.87
Range17.86
Interquartile range (IQR)6.785

Descriptive statistics

Standard deviation4.044785
Coefficient of variation (CV)0.87791354
Kurtosis-0.80317058
Mean4.6072703
Median Absolute Deviation (MAD)3.265
Skewness0.58626679
Sum13536.16
Variance16.360285
MonotonicityNot monotonic
2023-01-06T00:12:00.187142image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01 302
 
10.3%
7.3 21
 
0.7%
0.03 16
 
0.5%
0.02 14
 
0.5%
0.04 13
 
0.4%
0.09 13
 
0.4%
1.18 11
 
0.4%
0.06 11
 
0.4%
0.49 10
 
0.3%
0.17 10
 
0.3%
Other values (1066) 2517
85.7%
ValueCountFrequency (%)
0.01 302
10.3%
0.02 14
 
0.5%
0.03 16
 
0.5%
0.04 13
 
0.4%
0.05 10
 
0.3%
0.06 11
 
0.4%
0.07 5
 
0.2%
0.08 10
 
0.3%
0.09 13
 
0.4%
0.1 8
 
0.3%
ValueCountFrequency (%)
17.87 1
< 0.1%
17.31 1
< 0.1%
16.99 1
< 0.1%
16.58 1
< 0.1%
16.35 1
< 0.1%
15.52 1
< 0.1%
15.19 1
< 0.1%
15.14 1
< 0.1%
15.07 1
< 0.1%
15.04 2
0.1%

BMI
Real number (ℝ)

Distinct608
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.02015
Minimum1
Maximum87.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-06T00:12:00.326380image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.785
Q119
median43
Q356.1
95-th percentile64.715
Maximum87.3
Range86.3
Interquartile range (IQR)37.1

Descriptive statistics

Standard deviation20.175077
Coefficient of variation (CV)0.53064169
Kurtosis-1.3096342
Mean38.02015
Median Absolute Deviation (MAD)16.6
Skewness-0.20244667
Sum111703.2
Variance407.03372
MonotonicityNot monotonic
2023-01-06T00:12:00.463358image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.1 22
 
0.7%
4.1 20
 
0.7%
58.5 18
 
0.6%
55.8 16
 
0.5%
57 16
 
0.5%
54.2 15
 
0.5%
59.9 15
 
0.5%
59.3 14
 
0.5%
52.8 13
 
0.4%
59.4 13
 
0.4%
Other values (598) 2776
94.5%
ValueCountFrequency (%)
1 1
 
< 0.1%
1.4 2
 
0.1%
1.8 1
 
< 0.1%
1.9 1
 
< 0.1%
2 1
 
< 0.1%
2.1 11
0.4%
2.2 9
0.3%
2.3 6
0.2%
2.4 5
0.2%
2.5 8
0.3%
ValueCountFrequency (%)
87.3 1
< 0.1%
83.3 1
< 0.1%
82.8 1
< 0.1%
81.6 1
< 0.1%
79.3 1
< 0.1%
77.6 1
< 0.1%
77.3 1
< 0.1%
77.1 1
< 0.1%
76.7 1
< 0.1%
76.2 1
< 0.1%

Country
Categorical

Distinct193
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
Afghanistan
 
16
Peru
 
16
Nicaragua
 
16
Niger
 
16
Nigeria
 
16
Other values (188)
2858 

Length

Max length52
Median length34
Mean length10.041184
Min length4

Unique

Unique10 ?
Unique (%)0.3%

Sample

1st rowAfghanistan
2nd rowAfghanistan
3rd rowAfghanistan
4th rowAfghanistan
5th rowAfghanistan

Common Values

ValueCountFrequency (%)
Afghanistan 16
 
0.5%
Peru 16
 
0.5%
Nicaragua 16
 
0.5%
Niger 16
 
0.5%
Nigeria 16
 
0.5%
Norway 16
 
0.5%
Oman 16
 
0.5%
Pakistan 16
 
0.5%
Panama 16
 
0.5%
Papua New Guinea 16
 
0.5%
Other values (183) 2778
94.6%

Length

2023-01-06T00:12:00.726578image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Dev_Status
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
Developing
2426 
Developed
512 

Length

Max length10
Median length10
Mean length9.8257318
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDeveloping
2nd rowDeveloping
3rd rowDeveloping
4th rowDeveloping
5th rowDeveloping

Common Values

ValueCountFrequency (%)
Developing 2426
82.6%
Developed 512
 
17.4%

Length

2023-01-06T00:12:00.814737image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-06T00:12:00.914040image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Diphtheria
Real number (ℝ)

Distinct81
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.075221
Minimum2
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-06T00:12:01.026643image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile9
Q178
median93
Q397
95-th percentile99
Maximum99
Range97
Interquartile range (IQR)19

Descriptive statistics

Standard deviation23.917022
Coefficient of variation (CV)0.29140369
Kurtosis3.3990882
Mean82.075221
Median Absolute Deviation (MAD)6
Skewness-2.0395318
Sum241137
Variance572.02396
MonotonicityNot monotonic
2023-01-06T00:12:01.227999image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 350
 
11.9%
98 254
 
8.6%
97 205
 
7.0%
96 201
 
6.8%
95 200
 
6.8%
94 149
 
5.1%
93 120
 
4.1%
92 100
 
3.4%
91 91
 
3.1%
89 76
 
2.6%
Other values (71) 1192
40.6%
ValueCountFrequency (%)
2 1
 
< 0.1%
3 4
 
0.1%
4 12
 
0.4%
5 10
 
0.3%
6 16
 
0.5%
7 21
 
0.7%
8 39
1.3%
9 73
2.5%
16 1
 
< 0.1%
19 1
 
< 0.1%
ValueCountFrequency (%)
99 350
11.9%
98 254
8.6%
97 205
7.0%
96 201
6.8%
95 200
6.8%
94 149
5.1%
93 120
 
4.1%
92 100
 
3.4%
91 91
 
3.1%
89 76
 
2.6%

Education
Real number (ℝ)

Distinct173
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.935671
Minimum0
Maximum20.7
Zeros29
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-06T00:12:01.403158image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.6
Q110.1
median12.3
Q314.1
95-th percentile16.8
Maximum20.7
Range20.7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.3402021
Coefficient of variation (CV)0.27985039
Kurtosis0.92799932
Mean11.935671
Median Absolute Deviation (MAD)2
Skewness-0.62118269
Sum35067
Variance11.15695
MonotonicityNot monotonic
2023-01-06T00:12:01.532590image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.5 97
 
3.3%
11.8 81
 
2.8%
13 64
 
2.2%
12.9 58
 
2.0%
13.3 52
 
1.8%
12.8 46
 
1.6%
12.3 44
 
1.5%
12.6 43
 
1.5%
12.4 42
 
1.4%
11.9 41
 
1.4%
Other values (163) 2370
80.7%
ValueCountFrequency (%)
0 29
1.0%
2.8 1
 
< 0.1%
2.9 4
 
0.1%
3 1
 
< 0.1%
3.1 1
 
< 0.1%
3.3 1
 
< 0.1%
3.4 1
 
< 0.1%
3.5 3
 
0.1%
3.6 1
 
< 0.1%
3.7 2
 
0.1%
ValueCountFrequency (%)
20.7 1
 
< 0.1%
20.6 1
 
< 0.1%
20.5 1
 
< 0.1%
20.4 3
0.1%
20.3 4
0.1%
20.1 2
0.1%
19.8 1
 
< 0.1%
19.7 1
 
< 0.1%
19.5 3
0.1%
19.3 2
0.1%

GDP
Real number (ℝ)

Distinct2490
Distinct (%)84.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7475.5936
Minimum1.68135
Maximum119172.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-06T00:12:01.648884image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.68135
5-th percentile63.223863
Q1456.76653
median1680.8349
Q36454.0616
95-th percentile37813.234
Maximum119172.74
Range119171.06
Interquartile range (IQR)5997.2951

Descriptive statistics

Standard deviation13728.462
Coefficient of variation (CV)1.8364377
Kurtosis12.33742
Mean7475.5936
Median Absolute Deviation (MAD)1544.8364
Skewness3.1366648
Sum21963294
Variance1.8847067 × 108
MonotonicityNot monotonic
2023-01-06T00:12:01.769797image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1469.849149 49
 
1.7%
14672.8826 49
 
1.7%
3371.26869 49
 
1.7%
216.172747 34
 
1.2%
29986.2915 33
 
1.1%
18389.38433 33
 
1.1%
672.92113 18
 
0.6%
375.8528566 18
 
0.6%
4116.46693 17
 
0.6%
78.92744 17
 
0.6%
Other values (2480) 2621
89.2%
ValueCountFrequency (%)
1.68135 1
< 0.1%
3.685949 1
< 0.1%
4.6135745 1
< 0.1%
5.6687264 1
< 0.1%
8.376432 1
< 0.1%
11.147277 1
< 0.1%
11.33678 1
< 0.1%
11.553196 1
< 0.1%
11.631377 1
< 0.1%
12.1789279 1
< 0.1%
ValueCountFrequency (%)
119172.7418 1
< 0.1%
115761.577 1
< 0.1%
114293.8433 1
< 0.1%
113751.85 1
< 0.1%
89739.7117 1
< 0.1%
88564.82298 1
< 0.1%
87998.44468 1
< 0.1%
87646.75346 1
< 0.1%
86852.7119 1
< 0.1%
85948.746 1
< 0.1%

HepB
Real number (ℝ)

Distinct87
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.683799
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-06T00:12:01.903368image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q166
median89
Q396
95-th percentile99
Maximum99
Range98
Interquartile range (IQR)30

Descriptive statistics

Standard deviation28.851806
Coefficient of variation (CV)0.38121509
Kurtosis0.5971042
Mean75.683799
Median Absolute Deviation (MAD)9
Skewness-1.377775
Sum222359
Variance832.42671
MonotonicityNot monotonic
2023-01-06T00:12:02.014116image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 256
 
8.7%
98 243
 
8.3%
96 171
 
5.8%
95 162
 
5.5%
97 155
 
5.3%
93 133
 
4.5%
94 128
 
4.4%
92 108
 
3.7%
89 80
 
2.7%
91 75
 
2.6%
Other values (77) 1427
48.6%
ValueCountFrequency (%)
1 2
 
0.1%
2 32
1.1%
4 6
 
0.2%
5 19
 
0.6%
6 21
 
0.7%
7 43
1.5%
8 59
2.0%
9 75
2.6%
11 1
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
99 256
8.7%
98 243
8.3%
97 155
5.3%
96 171
5.8%
95 162
5.5%
94 128
4.4%
93 133
4.5%
92 108
3.7%
91 75
 
2.6%
89 80
 
2.7%

HIV/AIDS
Real number (ℝ)

Distinct200
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7421035
Minimum0.1
Maximum50.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-06T00:12:02.128797image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.1
median0.1
Q30.8
95-th percentile8.515
Maximum50.6
Range50.5
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation5.0777845
Coefficient of variation (CV)2.9147434
Kurtosis34.892008
Mean1.7421035
Median Absolute Deviation (MAD)0
Skewness5.396112
Sum5118.3
Variance25.783896
MonotonicityNot monotonic
2023-01-06T00:12:02.259371image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1 1781
60.6%
0.2 124
 
4.2%
0.3 115
 
3.9%
0.4 69
 
2.3%
0.5 42
 
1.4%
0.6 35
 
1.2%
0.9 32
 
1.1%
0.8 32
 
1.1%
0.7 29
 
1.0%
1.5 21
 
0.7%
Other values (190) 658
 
22.4%
ValueCountFrequency (%)
0.1 1781
60.6%
0.2 124
 
4.2%
0.3 115
 
3.9%
0.4 69
 
2.3%
0.5 42
 
1.4%
0.6 35
 
1.2%
0.7 29
 
1.0%
0.8 32
 
1.1%
0.9 32
 
1.1%
1 12
 
0.4%
ValueCountFrequency (%)
50.6 1
< 0.1%
50.3 1
< 0.1%
49.9 1
< 0.1%
49.1 1
< 0.1%
48.8 1
< 0.1%
46.4 1
< 0.1%
43.7 1
< 0.1%
43.5 1
< 0.1%
42.1 1
< 0.1%
40.7 1
< 0.1%

Income_Composition
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct625
Distinct (%)21.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.63141763
Minimum0
Maximum0.948
Zeros133
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-06T00:12:02.378580image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.268
Q10.494
median0.684
Q30.791
95-th percentile0.89
Maximum0.948
Range0.948
Interquartile range (IQR)0.297

Descriptive statistics

Standard deviation0.21092014
Coefficient of variation (CV)0.33404221
Kurtosis1.3725658
Mean0.63141763
Median Absolute Deviation (MAD)0.124
Skewness-1.1672968
Sum1855.105
Variance0.044487305
MonotonicityNot monotonic
2023-01-06T00:12:02.492787image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 133
 
4.5%
0.798 56
 
1.9%
0.791 55
 
1.9%
0.808 37
 
1.3%
0.455 21
 
0.7%
0.268 19
 
0.6%
0.7 17
 
0.6%
0.739 13
 
0.4%
0.714 12
 
0.4%
0.636 12
 
0.4%
Other values (615) 2563
87.2%
ValueCountFrequency (%)
0 133
4.5%
0.253 1
 
< 0.1%
0.255 1
 
< 0.1%
0.261 1
 
< 0.1%
0.266 1
 
< 0.1%
0.268 19
 
0.6%
0.27 1
 
< 0.1%
0.276 1
 
< 0.1%
0.278 1
 
< 0.1%
0.279 1
 
< 0.1%
ValueCountFrequency (%)
0.948 1
 
< 0.1%
0.945 1
 
< 0.1%
0.942 1
 
< 0.1%
0.941 1
 
< 0.1%
0.939 1
 
< 0.1%
0.938 1
 
< 0.1%
0.937 1
 
< 0.1%
0.936 5
0.2%
0.934 2
 
0.1%
0.933 1
 
< 0.1%

Infant_Deaths
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct209
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.303948
Minimum0
Maximum1800
Zeros848
Zeros (%)28.9%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-06T00:12:02.615894image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q322
95-th percentile94.15
Maximum1800
Range1800
Interquartile range (IQR)22

Descriptive statistics

Standard deviation117.9265
Coefficient of variation (CV)3.8914567
Kurtosis116.04276
Mean30.303948
Median Absolute Deviation (MAD)3
Skewness9.786963
Sum89033
Variance13906.66
MonotonicityNot monotonic
2023-01-06T00:12:02.730222image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 848
28.9%
1 342
 
11.6%
2 203
 
6.9%
3 175
 
6.0%
4 96
 
3.3%
8 57
 
1.9%
7 53
 
1.8%
9 48
 
1.6%
10 48
 
1.6%
6 46
 
1.6%
Other values (199) 1022
34.8%
ValueCountFrequency (%)
0 848
28.9%
1 342
11.6%
2 203
 
6.9%
3 175
 
6.0%
4 96
 
3.3%
5 44
 
1.5%
6 46
 
1.6%
7 53
 
1.8%
8 57
 
1.9%
9 48
 
1.6%
ValueCountFrequency (%)
1800 2
0.1%
1700 2
0.1%
1600 1
< 0.1%
1500 2
0.1%
1400 1
< 0.1%
1300 2
0.1%
1200 1
< 0.1%
1100 2
0.1%
1000 1
< 0.1%
957 1
< 0.1%

Life_Expectancy
Real number (ℝ)

Distinct362
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.195643
Minimum36.3
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-06T00:12:02.844127image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum36.3
5-th percentile51.385
Q163.025
median72
Q375.6
95-th percentile82
Maximum89
Range52.7
Interquartile range (IQR)12.575

Descriptive statistics

Standard deviation9.5366541
Coefficient of variation (CV)0.1378216
Kurtosis-0.24391734
Mean69.195643
Median Absolute Deviation (MAD)5.8
Skewness-0.63545466
Sum203296.8
Variance90.947771
MonotonicityNot monotonic
2023-01-06T00:12:02.965557image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73 45
 
1.5%
75 33
 
1.1%
78 31
 
1.1%
73.6 28
 
1.0%
73.9 25
 
0.9%
76 25
 
0.9%
81 25
 
0.9%
74.5 24
 
0.8%
74.7 24
 
0.8%
73.5 23
 
0.8%
Other values (352) 2655
90.4%
ValueCountFrequency (%)
36.3 1
< 0.1%
39 1
< 0.1%
41 1
< 0.1%
41.5 1
< 0.1%
42.3 1
< 0.1%
43.1 1
< 0.1%
43.3 1
< 0.1%
43.5 1
< 0.1%
43.8 1
< 0.1%
44 1
< 0.1%
ValueCountFrequency (%)
89 11
0.4%
88 10
0.3%
87 9
0.3%
86 15
0.5%
85 12
0.4%
84 11
0.4%
83.7 1
 
< 0.1%
83.5 2
 
0.1%
83.4 1
 
< 0.1%
83.3 1
 
< 0.1%

lt5_Deaths
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct252
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.035739
Minimum0
Maximum2500
Zeros785
Zeros (%)26.7%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-06T00:12:03.073727image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q328
95-th percentile138
Maximum2500
Range2500
Interquartile range (IQR)28

Descriptive statistics

Standard deviation160.44555
Coefficient of variation (CV)3.8168842
Kurtosis109.7528
Mean42.035739
Median Absolute Deviation (MAD)4
Skewness9.4950647
Sum123501
Variance25742.774
MonotonicityNot monotonic
2023-01-06T00:12:03.338321image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 785
26.7%
1 361
 
12.3%
2 163
 
5.5%
4 161
 
5.5%
3 129
 
4.4%
12 53
 
1.8%
8 49
 
1.7%
6 48
 
1.6%
10 47
 
1.6%
5 44
 
1.5%
Other values (242) 1098
37.4%
ValueCountFrequency (%)
0 785
26.7%
1 361
12.3%
2 163
 
5.5%
3 129
 
4.4%
4 161
 
5.5%
5 44
 
1.5%
6 48
 
1.6%
7 30
 
1.0%
8 49
 
1.7%
9 40
 
1.4%
ValueCountFrequency (%)
2500 1
< 0.1%
2400 1
< 0.1%
2300 1
< 0.1%
2200 1
< 0.1%
2100 1
< 0.1%
2000 2
0.1%
1900 1
< 0.1%
1800 1
< 0.1%
1700 1
< 0.1%
1600 1
< 0.1%

Measles
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct958
Distinct (%)32.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2419.5922
Minimum0
Maximum212183
Zeros983
Zeros (%)33.5%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-06T00:12:03.517690image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median17
Q3360.25
95-th percentile9985.55
Maximum212183
Range212183
Interquartile range (IQR)360.25

Descriptive statistics

Standard deviation11467.272
Coefficient of variation (CV)4.7393409
Kurtosis114.8599
Mean2419.5922
Median Absolute Deviation (MAD)17
Skewness9.4413319
Sum7108762
Variance1.3149834 × 108
MonotonicityNot monotonic
2023-01-06T00:12:03.682771image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 983
33.5%
1 104
 
3.5%
2 68
 
2.3%
3 44
 
1.5%
4 33
 
1.1%
6 29
 
1.0%
7 28
 
1.0%
5 25
 
0.9%
8 24
 
0.8%
9 22
 
0.7%
Other values (948) 1578
53.7%
ValueCountFrequency (%)
0 983
33.5%
1 104
 
3.5%
2 68
 
2.3%
3 44
 
1.5%
4 33
 
1.1%
5 25
 
0.9%
6 29
 
1.0%
7 28
 
1.0%
8 24
 
0.8%
9 22
 
0.7%
ValueCountFrequency (%)
212183 1
< 0.1%
182485 1
< 0.1%
168107 1
< 0.1%
141258 1
< 0.1%
133802 1
< 0.1%
131441 1
< 0.1%
124219 1
< 0.1%
118712 1
< 0.1%
110927 1
< 0.1%
109023 1
< 0.1%

Percent_Expenditure
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2328
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean738.2513
Minimum0
Maximum19479.912
Zeros611
Zeros (%)20.8%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-06T00:12:03.826372image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.6853426
median64.912906
Q3441.53414
95-th percentile4506.6385
Maximum19479.912
Range19479.912
Interquartile range (IQR)436.8488

Descriptive statistics

Standard deviation1987.9149
Coefficient of variation (CV)2.6927347
Kurtosis26.573387
Mean738.2513
Median Absolute Deviation (MAD)64.912906
Skewness4.6520513
Sum2168982.3
Variance3951805.5
MonotonicityNot monotonic
2023-01-06T00:12:03.960548image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 611
 
20.8%
71.27962362 1
 
< 0.1%
3.304039899 1
 
< 0.1%
218.5716179 1
 
< 0.1%
36.81621175 1
 
< 0.1%
2.542436908 1
 
< 0.1%
2.092343893 1
 
< 0.1%
22.35595448 1
 
< 0.1%
15.25518816 1
 
< 0.1%
31.50243237 1
 
< 0.1%
Other values (2318) 2318
78.9%
ValueCountFrequency (%)
0 611
20.8%
0.09987219 1
 
< 0.1%
0.108055973 1
 
< 0.1%
0.27564826 1
 
< 0.1%
0.328418056 1
 
< 0.1%
0.358651421 1
 
< 0.1%
0.388253772 1
 
< 0.1%
0.397228764 1
 
< 0.1%
0.442802404 1
 
< 0.1%
0.5305728 1
 
< 0.1%
ValueCountFrequency (%)
19479.91161 1
< 0.1%
19099.04506 1
< 0.1%
18961.3486 1
< 0.1%
18822.86732 1
< 0.1%
18379.32974 1
< 0.1%
17028.52798 1
< 0.1%
16255.16198 1
< 0.1%
15515.75234 1
< 0.1%
15345.4907 1
< 0.1%
15268.06445 1
< 0.1%

Polio
Real number (ℝ)

Distinct73
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.307692
Minimum3
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-06T00:12:04.104832image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile9
Q177
median93
Q397
95-th percentile99
Maximum99
Range96
Interquartile range (IQR)20

Descriptive statistics

Standard deviation23.636677
Coefficient of variation (CV)0.28717458
Kurtosis3.6137881
Mean82.307692
Median Absolute Deviation (MAD)6
Skewness-2.0664036
Sum241820
Variance558.69249
MonotonicityNot monotonic
2023-01-06T00:12:04.219171image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 376
 
12.8%
98 255
 
8.7%
96 207
 
7.0%
97 205
 
7.0%
95 180
 
6.1%
94 159
 
5.4%
93 120
 
4.1%
92 96
 
3.3%
91 88
 
3.0%
9 77
 
2.6%
Other values (63) 1175
40.0%
ValueCountFrequency (%)
3 7
 
0.2%
4 11
 
0.4%
5 8
 
0.3%
6 11
 
0.4%
7 24
 
0.8%
8 40
1.4%
9 77
2.6%
17 1
 
< 0.1%
23 1
 
< 0.1%
24 2
 
0.1%
ValueCountFrequency (%)
99 376
12.8%
98 255
8.7%
97 205
7.0%
96 207
7.0%
95 180
6.1%
94 159
5.4%
93 120
 
4.1%
92 96
 
3.3%
91 88
 
3.0%
89 56
 
1.9%

Population
Real number (ℝ)

Distinct2278
Distinct (%)77.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13168705
Minimum34
Maximum1.2938593 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-06T00:12:04.335704image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum34
5-th percentile8446
Q1136425.5
median1289898
Q37394105.5
95-th percentile49175848
Maximum1.2938593 × 109
Range1.2938593 × 109
Interquartile range (IQR)7257680

Descriptive statistics

Standard deviation56299844
Coefficient of variation (CV)4.2752757
Kurtosis319.83093
Mean13168705
Median Absolute Deviation (MAD)1271335
Skewness15.921272
Sum3.8689655 × 1010
Variance3.1696724 × 1015
MonotonicityNot monotonic
2023-01-06T00:12:04.462951image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49175848 65
 
2.2%
8446 49
 
1.7%
1289898 49
 
1.7%
18563 49
 
1.7%
943286 49
 
1.7%
82573 34
 
1.2%
8486 33
 
1.1%
4564297 33
 
1.1%
11719673 18
 
0.6%
542357 18
 
0.6%
Other values (2268) 2541
86.5%
ValueCountFrequency (%)
34 1
< 0.1%
36 1
< 0.1%
41 1
< 0.1%
43 1
< 0.1%
123 1
< 0.1%
135 1
< 0.1%
146 1
< 0.1%
286 1
< 0.1%
292 2
0.1%
297 1
< 0.1%
ValueCountFrequency (%)
1293859294 1
< 0.1%
1179681239 1
< 0.1%
1161977719 1
< 0.1%
1144118674 1
< 0.1%
1126135777 1
< 0.1%
258162113 1
< 0.1%
255131116 1
< 0.1%
248883232 1
< 0.1%
242524123 1
< 0.1%
236159276 1
< 0.1%

Thinness_1-19y
Real number (ℝ)

Distinct200
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9559564
Minimum0.1
Maximum27.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-06T00:12:04.578230image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.6
Q11.6
median3.4
Q37.3
95-th percentile14.7
Maximum27.7
Range27.6
Interquartile range (IQR)5.7

Descriptive statistics

Standard deviation4.5414034
Coefficient of variation (CV)0.91635257
Kurtosis3.3866731
Mean4.9559564
Median Absolute Deviation (MAD)2.4
Skewness1.6364319
Sum14560.6
Variance20.624345
MonotonicityNot monotonic
2023-01-06T00:12:04.680287image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 74
 
2.5%
1.9 65
 
2.2%
0.8 64
 
2.2%
0.7 63
 
2.1%
1.2 62
 
2.1%
2.1 61
 
2.1%
1.5 60
 
2.0%
2.2 58
 
2.0%
2 57
 
1.9%
0.9 57
 
1.9%
Other values (190) 2317
78.9%
ValueCountFrequency (%)
0.1 28
 
1.0%
0.2 41
1.4%
0.3 33
1.1%
0.4 5
 
0.2%
0.5 35
1.2%
0.6 41
1.4%
0.7 63
2.1%
0.8 64
2.2%
0.9 57
1.9%
1 74
2.5%
ValueCountFrequency (%)
27.7 1
 
< 0.1%
27.5 1
 
< 0.1%
27.4 1
 
< 0.1%
27.3 1
 
< 0.1%
27.2 2
0.1%
27.1 2
0.1%
27 3
0.1%
26.9 2
0.1%
26.8 2
0.1%
26.7 1
 
< 0.1%

Thinness_5-9y
Real number (ℝ)

Distinct207
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0025528
Minimum0.1
Maximum28.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-06T00:12:04.797696image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.5
Q11.6
median3.4
Q37.3
95-th percentile15.015
Maximum28.6
Range28.5
Interquartile range (IQR)5.7

Descriptive statistics

Standard deviation4.6701535
Coefficient of variation (CV)0.93355408
Kurtosis3.7376175
Mean5.0025528
Median Absolute Deviation (MAD)2.4
Skewness1.713845
Sum14697.5
Variance21.810334
MonotonicityNot monotonic
2023-01-06T00:12:04.908590image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9 69
 
2.3%
1.1 67
 
2.3%
0.5 63
 
2.1%
1.9 63
 
2.1%
1 62
 
2.1%
2.1 61
 
2.1%
1.3 59
 
2.0%
1.5 57
 
1.9%
1.7 55
 
1.9%
0.6 54
 
1.8%
Other values (197) 2328
79.2%
ValueCountFrequency (%)
0.1 37
1.3%
0.2 46
1.6%
0.3 26
 
0.9%
0.4 17
 
0.6%
0.5 63
2.1%
0.6 54
1.8%
0.7 46
1.6%
0.8 36
1.2%
0.9 69
2.3%
1 62
2.1%
ValueCountFrequency (%)
28.6 1
< 0.1%
28.5 1
< 0.1%
28.4 1
< 0.1%
28.3 1
< 0.1%
28.2 1
< 0.1%
28.1 1
< 0.1%
28 2
0.1%
27.9 1
< 0.1%
27.8 2
0.1%
27.7 1
< 0.1%

Total_Expenditure
Real number (ℝ)

Distinct818
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.905211
Minimum0.37
Maximum17.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-06T00:12:05.029272image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.37
5-th percentile1.9685
Q14.26
median5.71
Q37.44
95-th percentile9.7415
Maximum17.6
Range17.23
Interquartile range (IQR)3.18

Descriptive statistics

Standard deviation2.48462
Coefficient of variation (CV)0.42075041
Kurtosis1.3511628
Mean5.905211
Median Absolute Deviation (MAD)1.55
Skewness0.67536079
Sum17349.51
Variance6.1733364
MonotonicityNot monotonic
2023-01-06T00:12:05.135368image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.56 24
 
0.8%
6.31 23
 
0.8%
4.6 16
 
0.5%
6.7 15
 
0.5%
2.77 15
 
0.5%
5.6 12
 
0.4%
3.4 12
 
0.4%
5.9 11
 
0.4%
4.36 11
 
0.4%
5.25 11
 
0.4%
Other values (808) 2788
94.9%
ValueCountFrequency (%)
0.37 1
 
< 0.1%
0.65 1
 
< 0.1%
0.74 1
 
< 0.1%
0.76 1
 
< 0.1%
0.92 1
 
< 0.1%
1.1 3
0.1%
1.12 3
0.1%
1.15 2
0.1%
1.17 2
0.1%
1.18 3
0.1%
ValueCountFrequency (%)
17.6 1
< 0.1%
17.24 2
0.1%
17.2 2
0.1%
17.14 1
< 0.1%
17 1
< 0.1%
16.9 1
< 0.1%
16.61 2
0.1%
16.2 1
< 0.1%
15.6 1
< 0.1%
15.57 1
< 0.1%

Unnamed: 0
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct2938
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1468.5
Minimum0
Maximum2937
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-06T00:12:05.249935image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile146.85
Q1734.25
median1468.5
Q32202.75
95-th percentile2790.15
Maximum2937
Range2937
Interquartile range (IQR)1468.5

Descriptive statistics

Standard deviation848.27187
Coefficient of variation (CV)0.57764513
Kurtosis-1.2
Mean1468.5
Median Absolute Deviation (MAD)734.5
Skewness0
Sum4314453
Variance719565.17
MonotonicityStrictly increasing
2023-01-06T00:12:05.355817image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
1951 1
 
< 0.1%
1953 1
 
< 0.1%
1954 1
 
< 0.1%
1955 1
 
< 0.1%
1956 1
 
< 0.1%
1957 1
 
< 0.1%
1958 1
 
< 0.1%
1959 1
 
< 0.1%
1960 1
 
< 0.1%
Other values (2928) 2928
99.7%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
2937 1
< 0.1%
2936 1
< 0.1%
2935 1
< 0.1%
2934 1
< 0.1%
2933 1
< 0.1%
2932 1
< 0.1%
2931 1
< 0.1%
2930 1
< 0.1%
2929 1
< 0.1%
2928 1
< 0.1%

Year
Real number (ℝ)

Distinct16
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2007.5187
Minimum2000
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-01-06T00:12:05.462954image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2000
Q12004
median2008
Q32012
95-th percentile2015
Maximum2015
Range15
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.6138409
Coefficient of variation (CV)0.0022982804
Kurtosis-1.2137217
Mean2007.5187
Median Absolute Deviation (MAD)4
Skewness-0.0064090274
Sum5898090
Variance21.287528
MonotonicityNot monotonic
2023-01-06T00:12:05.543813image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2013 193
 
6.6%
2015 183
 
6.2%
2014 183
 
6.2%
2012 183
 
6.2%
2011 183
 
6.2%
2010 183
 
6.2%
2009 183
 
6.2%
2008 183
 
6.2%
2007 183
 
6.2%
2006 183
 
6.2%
Other values (6) 1098
37.4%
ValueCountFrequency (%)
2000 183
6.2%
2001 183
6.2%
2002 183
6.2%
2003 183
6.2%
2004 183
6.2%
2005 183
6.2%
2006 183
6.2%
2007 183
6.2%
2008 183
6.2%
2009 183
6.2%
ValueCountFrequency (%)
2015 183
6.2%
2014 183
6.2%
2013 193
6.6%
2012 183
6.2%
2011 183
6.2%
2010 183
6.2%
2009 183
6.2%
2008 183
6.2%
2007 183
6.2%
2006 183
6.2%

Interactions

2023-01-06T00:11:56.778686image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:10.402214image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:13.038350image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:15.229192image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:17.415558image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:19.720402image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:22.031274image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:24.507134image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:26.582682image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:28.811129image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:30.875902image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:33.061000image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:35.301137image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:37.502086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:40.057297image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:42.446487image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:44.733143image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:47.191257image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:49.255123image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:52.230326image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:54.378055image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:56.891423image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:10.517718image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:13.144551image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:15.333565image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:17.522976image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:19.828300image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:22.149670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:24.613975image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:26.685737image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:28.911230image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:30.982936image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:33.308954image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:35.406147image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:37.604678image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:40.182563image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:42.558439image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:44.886049image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:47.287981image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:49.355167image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:52.336679image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:54.518355image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:56.991810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:10.624003image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:13.255466image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:15.434633image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:17.630359image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:19.930269image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:22.269862image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:24.716038image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:26.790149image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:29.015230image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:31.098868image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:33.415791image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:35.511624image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:37.717580image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:40.302945image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:42.656463image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:45.239260image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:47.385060image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:49.520257image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:52.458669image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:54.639760image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:57.084863image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:10.754668image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:13.349058image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:15.527486image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:17.726810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:20.021139image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:22.568176image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:24.808619image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:26.882101image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:29.107105image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:31.194236image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:33.505312image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:35.606018image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:37.826190image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:40.453980image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:42.745126image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:45.370352image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:47.469828image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:49.761988image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:52.563204image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:54.755767image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:57.244498image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:10.902466image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:13.454846image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:15.622547image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:17.821315image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:20.113242image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:22.706959image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:24.904351image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:26.976484image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:29.203056image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:31.292701image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:33.605980image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:35.701426image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:37.942695image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:40.586951image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:42.838120image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:45.500172image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:47.571142image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:49.890435image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:52.673935image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:54.866213image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:57.353269image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:11.207259image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:13.551901image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:15.708714image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:17.918487image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:20.204543image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:22.812385image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:24.999814image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:27.070561image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:29.295233image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:31.388196image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:33.701013image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:35.794227image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:38.107598image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:40.690722image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:42.924176image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:45.589483image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:47.667072image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:50.104710image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:52.786651image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:55.007623image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:57.465955image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:11.354313image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:13.656242image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:15.802767image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:18.015685image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:20.307420image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:22.933096image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:25.097515image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:27.167016image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:29.394786image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:31.493535image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-01-06T00:11:45.709223image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-01-06T00:11:23.059943image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-01-06T00:11:27.264988image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:29.489714image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:31.594213image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:33.892759image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:35.991424image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:38.391052image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:40.890655image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:43.140936image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-01-06T00:11:47.886282image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:50.425154image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-01-06T00:11:57.664579image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:11.615240image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:13.857594image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:16.003473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:18.260794image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:20.513341image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-01-06T00:11:51.716355image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:53.956731image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:56.230785image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:58.782326image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:12.707389image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:14.905334image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:17.106813image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:19.410794image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:21.667784image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:24.188827image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:26.272974image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:28.501807image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:30.572718image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:32.735929image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:34.990328image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:37.150975image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:39.733145image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:42.121606image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:44.341749image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:46.892839image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:48.933491image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:51.828367image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:54.055815image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:56.325148image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:58.887199image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:12.817492image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:15.017942image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:17.209643image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:19.517341image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:21.808338image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:24.295392image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:26.376327image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:28.602297image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:30.676354image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:32.842442image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:35.094387image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:37.273654image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:39.844452image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:42.229387image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:44.441185image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:46.994002image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:49.049322image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:51.967726image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:54.160855image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:56.567406image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:58.986868image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:12.929744image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:15.119108image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:17.308867image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:19.618185image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:21.924396image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:24.404238image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:26.473543image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:28.702056image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:30.772990image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:32.948356image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:35.197466image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:37.395600image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:39.940273image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:42.333167image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:44.569013image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:47.089879image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:49.144787image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:52.100401image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:54.261087image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-06T00:11:56.675409image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-01-06T00:12:05.650207image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Adult_MortalityAlcoholBMIDiphtheriaEducationGDPHepBHIV/AIDSIncome_CompositionInfant_DeathsLife_Expectancylt5_DeathsMeaslesPercent_ExpenditurePolioPopulationThinness_1-19yThinness_5-9yTotal_ExpenditureUnnamed: 0YearDev_Status
Adult_Mortality1.000-0.200-0.401-0.329-0.489-0.346-0.2190.521-0.5330.390-0.6480.4030.146-0.297-0.3200.0220.3990.413-0.1750.027-0.0530.364
Alcohol-0.2001.0000.2940.2630.5010.3810.108-0.1840.494-0.3610.416-0.360-0.1950.2630.2530.015-0.434-0.4270.332-0.048-0.0920.631
BMI-0.4010.2941.0000.3420.5990.3860.266-0.5240.584-0.4890.585-0.501-0.2760.2820.332-0.019-0.574-0.5840.2510.0020.1470.459
Diphtheria-0.3290.2630.3421.0000.5320.3380.746-0.4700.516-0.4230.541-0.426-0.2530.2270.921-0.053-0.246-0.2540.153-0.0090.1390.315
Education-0.4890.5010.5990.5321.0000.6040.382-0.6140.881-0.5950.791-0.606-0.2880.4660.529-0.018-0.571-0.5720.267-0.0430.1860.602
GDP-0.3460.3810.3860.3380.6041.0000.229-0.4330.661-0.4170.567-0.422-0.1690.5750.348-0.015-0.381-0.3880.127-0.0490.1480.429
HepB-0.2190.1080.2660.7460.3820.2291.000-0.3720.354-0.3360.363-0.338-0.2590.1130.719-0.067-0.116-0.1260.104-0.0090.2290.228
HIV/AIDS0.521-0.184-0.524-0.470-0.614-0.433-0.3721.000-0.6310.487-0.7490.5120.204-0.255-0.4830.0640.4830.471-0.1430.008-0.0560.126
Income_Composition-0.5330.4940.5840.5160.8810.6610.354-0.6311.000-0.5410.835-0.552-0.2030.4160.5220.034-0.574-0.5750.221-0.0220.1850.686
Infant_Deaths0.390-0.361-0.489-0.423-0.595-0.417-0.3360.487-0.5411.000-0.5940.9930.573-0.361-0.4260.3180.4660.480-0.2140.040-0.0520.065
Life_Expectancy-0.6480.4160.5850.5410.7910.5670.363-0.7490.835-0.5941.000-0.612-0.2770.4290.531-0.015-0.610-0.6200.281-0.0290.1530.627
lt5_Deaths0.403-0.360-0.501-0.426-0.606-0.422-0.3380.512-0.5520.993-0.6121.0000.574-0.362-0.4300.3110.4740.487-0.2200.031-0.0520.060
Measles0.146-0.195-0.276-0.253-0.288-0.169-0.2590.204-0.2030.573-0.2770.5741.000-0.153-0.2550.2450.3120.325-0.1750.058-0.0950.022
Percent_Expenditure-0.2970.2630.2820.2270.4660.5750.113-0.2550.416-0.3610.429-0.362-0.1531.0000.2130.008-0.307-0.3090.161-0.101-0.0500.448
Polio-0.3200.2530.3320.9210.5290.3480.719-0.4830.522-0.4260.531-0.430-0.2550.2131.000-0.068-0.233-0.2430.147-0.0100.1160.306
Population0.0220.015-0.019-0.053-0.018-0.015-0.0670.0640.0340.318-0.0150.3110.2450.008-0.0681.000-0.0070.002-0.0210.0170.0320.067
Thinness_1-19y0.399-0.434-0.574-0.246-0.571-0.381-0.1160.483-0.5740.466-0.6100.4740.312-0.307-0.233-0.0071.0000.949-0.3430.026-0.0410.464
Thinness_5-9y0.413-0.427-0.584-0.254-0.572-0.388-0.1260.471-0.5750.480-0.6200.4870.325-0.309-0.2430.0020.9491.000-0.3570.041-0.0400.467
Total_Expenditure-0.1750.3320.2510.1530.2670.1270.104-0.1430.221-0.2140.281-0.220-0.1750.1610.147-0.021-0.343-0.3571.0000.0150.0610.404
Unnamed: 00.027-0.0480.002-0.009-0.043-0.049-0.0090.008-0.0220.040-0.0290.0310.058-0.101-0.0100.0170.0260.0410.0151.000-0.0040.148
Year-0.053-0.0920.1470.1390.1860.1480.229-0.0560.185-0.0520.153-0.052-0.095-0.0500.1160.032-0.041-0.0400.061-0.0041.0000.000
Dev_Status0.3640.6310.4590.3150.6020.4290.2280.1260.6860.0650.6270.0600.0220.4480.3060.0670.4640.4670.4040.1480.0001.000

Missing values

2023-01-06T00:11:59.219375image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-06T00:11:59.550625image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0CountryYearDev_StatusLife_ExpectancyAdult_MortalityInfant_DeathsAlcoholPercent_ExpenditureHepBMeaslesBMIlt5_DeathsPolioTotal_ExpenditureDiphtheriaHIV/AIDSGDPPopulationThinness_1-19yThinness_5-9yIncome_CompositionEducation
00Afghanistan2015Developing65.0263.0620.0171.27962465.0115419.1836.08.1665.00.1584.25921033736494.017.217.30.47910.1
11Afghanistan2014Developing59.9271.0640.0173.52358262.049218.68658.08.1862.00.1612.696514327582.017.517.50.47610.0
22Afghanistan2013Developing59.9268.0660.0173.21924364.043018.18962.08.1364.00.1631.74497631731688.017.717.70.4709.9
33Afghanistan2012Developing59.5272.0690.0178.18421567.0278717.69367.08.5267.00.1669.9590003696958.017.918.00.4639.8
44Afghanistan2011Developing59.2275.0710.017.09710968.0301317.29768.07.8768.00.163.5372312978599.018.218.20.4549.5
55Afghanistan2010Developing58.8279.0740.0179.67936766.0198916.710266.09.2066.00.1553.3289402883167.018.418.40.4489.2
66Afghanistan2009Developing58.6281.0770.0156.76221763.0286116.210663.09.4263.00.1445.893298284331.018.618.70.4348.9
77Afghanistan2008Developing58.1287.0800.0325.87392564.0159915.711064.08.3364.00.1373.3611162729431.018.818.90.4338.7
88Afghanistan2007Developing57.5295.0820.0210.91015663.0114115.211363.06.7363.00.1369.83579626616792.019.019.10.4158.4
99Afghanistan2006Developing57.3295.0840.0317.17151864.0199014.711658.07.4358.00.1272.5637702589345.019.219.30.4058.1
Unnamed: 0CountryYearDev_StatusLife_ExpectancyAdult_MortalityInfant_DeathsAlcoholPercent_ExpenditureHepBMeaslesBMIlt5_DeathsPolioTotal_ExpenditureDiphtheriaHIV/AIDSGDPPopulationThinness_1-19yThinness_5-9yIncome_CompositionEducation
29282928Zimbabwe2009Developing50.0587.0304.641.04002173.085329.04569.06.2673.018.165.8241211381599.07.57.40.4199.9
29292929Zimbabwe2008Developing48.2632.0303.5620.84342975.0028.64675.04.9675.020.5325.67857313558469.07.87.80.4219.7
29302930Zimbabwe2007Developing46.667.0293.8829.81456672.024228.24673.04.4773.023.7396.9982171332999.08.28.20.4149.6
29312931Zimbabwe2006Developing45.47.0284.5734.26216968.021227.94571.05.127.026.8414.79623213124267.08.68.60.4089.5
29322932Zimbabwe2005Developing44.6717.0284.148.71740965.042027.54369.06.4468.030.3444.765750129432.09.09.00.4069.3
29332933Zimbabwe2004Developing44.3723.0274.360.00000068.03127.14267.07.1365.033.6454.36665412777511.09.49.40.4079.2
29342934Zimbabwe2003Developing44.5715.0264.060.0000007.099826.7417.06.5268.036.7453.35115512633897.09.89.90.4189.5
29352935Zimbabwe2002Developing44.873.0254.430.00000073.030426.34073.06.5371.039.857.348340125525.01.21.30.42710.0
29362936Zimbabwe2001Developing45.3686.0251.720.00000076.052925.93976.06.1675.042.1548.58731212366165.01.61.70.4279.8
29372937Zimbabwe2000Developing46.0665.0241.680.00000079.0148325.53978.07.1078.043.5547.35887812222251.011.011.20.4349.8